Throughout the course of diagnosis and treatment of various disorders, e.g., cancer, a patient must undergo many different medical scans. Each one of these medical images possesses different information so it is advantageous to combine information from them.
The information in the scans is stored as image intensity values at three dimensional (3-D) coordinates. However, the body is not fixed in these coordinates. Differences in patient setup and body deformations make it difficult to directly relate the position of a specific point in the anatomy on one image to the next. The images can be rigidly aligned for a specific part of the anatomy, but this alignment will not be globally accurate. Deformable image registration (DIR) algorithms can be used to warp one of the images (the moving image) so that the alignment of the scans is accurate throughout the entirety of the other image (static image), meaning that the same tissue is located at the same coordinate in both images. To accomplish this, DIR finds a mapping solution that matches the features of one image to those of another. This deformable registration can then be used to transfer information. In radiation oncology, for example, when dealing with cancer recurrence, it is important to map any previously delivered radiation dose to new patient image. This will help the treatment planner spare organs and tissues that have already received a dose close to tolerance.
DIR is already used clinically and the theoretical deformations from these registrations have been shown to be drastically different from the actual deformation. Thus, errors therein could affect patient treatment. However, there currently is no clinical standard for providing quality assurance (QA) of DIR.
Currently, one method of providing QA for DIR involves the use of a phantom that represents a single two-dimensional slice of the body. Although the phantom is constructed in three dimensions, it and its deformations are symmetric with respect to the axial direction, making it function as a two-dimensional (2-D) system.
While the use of such a 2-D system has provided some insights into DIR algorithms its applicability and usefulness is limited as it is not completely compatible with DIR algorithms. Specifically, DIR algorithms are frequently customized in order to be used with the 2-D system. These customizations decrease the value of the 2-D system as it is unclear whether discrepancies between the theoretical and actual deformation are the result of the customization or the result of the algorithm. Accordingly, improved apparatuses, systems, and methods are required.